AntiPatterns: refactoring software, architectures, and projects in crisis
AntiPatterns: refactoring software, architectures, and projects in crisis
Future Generation Computer Systems
IEEE Transactions on Software Engineering
Interactive, Evolutionary Search in Upstream Object-Oriented Class Design
IEEE Transactions on Software Engineering
A tutorial for competent memetic algorithms: model, taxonomy, and design issues
IEEE Transactions on Evolutionary Computation
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Recent research has demonstrated that the problem of class modelling within early cycle object orientated software engineering can be successfully tackled by posing it as a search problem to be tackled with meta-heuristics. This "Search Based Software Engineering" approach has been illustrated using both Evolutionary Algorithms and Ant Colony Optimisation to perform the underlying search. Each has been shown to display strengths and weaknesses - both in terms of how easily "standard" algorithms can be applied to the domain, and of optimisation performance. This paper extends that work by considering the effect of incorporating Local Search. Specifically we examine the hypothesis that within a memetic framework the choice of global search heuristic does not significantly affect search performance, freeing the decision to be made on other more subjective factors. Results show that in fact the use of local search is not always beneficial to the Ant Colony Algorithm, whereas for the Evolutionary Algorithm with order based recombination it is highly effective at improving both the quality and speed of optimisation. Across a range of parameter settings ACO found its best solutions earlier than EAs, but those solutions were of lower quality than those found by EAs. For both algorithms we demonstrated that the number of constraints present, which relates to the number of classes created, has a far bigger impact on solution quality and time than the size of the problem in terms of numbers of attributes and methods.